Chess playing robotic arm

Atas, Musa; Dogan, Yahya; Atas, Ssa

doi:10.1109/siu.2014.6830443

Cited by 5 publications

(3 citation statements)

References 3 publications

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“…it is aimed to extract the spectral and shape properties of buildings by using techniques such as [1]. With the rapid development of deep learning recently, it has made great progress in the fields of agriculture [2][3], health [4], robotics and security [5], natural language processing [6], as well as in the fields of remote sensing, semantic segmentation and target recognition [1]. Deep learning methods are in the direction of producing more stable results for image recognition in remote sensing with high accuracy and high computational speeds [1].…”

Section: Introductionmentioning

confidence: 99%

Performance Evaluation of Jaccard-Dice Coefficient on Building Segmentation from High Resolution Satellite Images

Ataş

2023

Balkan Journal of Electrical and Computer Engineering

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In remote sensing applications, segmentation of input satellite images according to semantic information and estimating the semantic category of each pixel from a given set of tags are of great importance for the automatic tracking task. It is important in situations such as building detection from high resolution satellite images, city planning, environmental preparation, disaster management. Buildings in metropolitan areas are crowded and messy, so high-resolution images from satellites need to be automated to detect buildings. Segmentation of remote sensing images with deep learning technology has been a widely considered area of research. The Fully Convolutional Network (FCN) model, a popular segmentation model, is used for building detection based on pixel-level satellite images. In the U-Net model developed for biomedical image segmentation and modified in our study, its performances during training, accuracy and testing were compared by using customized loss functions such as Dice Coefficient and Jaccard Index measurements. Dice Coefficient loss score was obtained 84% and Jaccard Index lost score was obtained 70%. In addition, the Dice Coefficient loss score increased from 84% to 87% by using the Batch Normalization (BN) method instead of the Dropout method in the model.

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Section: Introductionmentioning

confidence: 99%

Performance Evaluation of Jaccard-Dice Coefficient on Building Segmentation from High Resolution Satellite Images

Ataş

2023

Balkan Journal of Electrical and Computer Engineering

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“…Arm robot often called industrial robot (defined by ISO 8373) is widely used in the industry [2] especially in pick and place task [3]. Arm Robot is reliable for pick and place task because it is considered capable of replacing human arm [4]. In order to develop an efficient arm robot control system without damage risk, the control system should be tested and implemented to arm robot model [5].…”

Section: Introductionmentioning

confidence: 99%

Pattern recognition based movement control and gripping forces control system on arm robot model using LabVIEW

Ramadhan

Lilansa

Rifa'i

et al. 2022

J. Mechatron. Electr. Power Veh. Technol.

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Most arm robot has an inefficient operating time because it requires operator to input destination coordinates. Besides, main problem of arm robot is object’s vulnerability when it is manipulated by the robot. This research goals is to develop an arm robot control system which has ability to automatically detect object using image processing in order to reduce operating time. It is also able to control gripping force for eliminating damage to objects caused by robot gripper. This research is implemented in LabVIEW 2011 software to control arm robot model which can represent industrial scale robot. The software is designed with informative visualization to help user learn and understand robotic control concept deeply. The system can automatically detect object position based on pattern recognition method which has four steps: pre-processing process to initialize picture taken by camera, segmentation process for separating object from the background, classification process to determine characteristics of object, and position estimation process to estimate object position in the picture. The object’s position data are then calculated by using kinematic equation to control the robot’s motion. The results show that the system is able to detect object and move the robot automatically with accuracy rate in x-axis is 95.578 % and in y-axis is 92.878 %. The system also implements modified PI control method with FSR as input to control gripping force with maximum overshoot value 10 %. Arm robot model control system developed is successfully meet the expectation. The system control can be implemented to industrial scale arm robot with several modification because of kinematic similarity between model and industrial scale robot.

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“…Yapay zeka (YZ) ve makine öğrenmesi (MO), tarım sektöründe devrim niteliğinde bir değişim sağlama potansiyeline sahiptir ve hassas tarım kapsamında ele alınmalıdır. Günümüzde yapay zeka bir çok alanda başarılı ve etkili bir şekilde uygulanmaktadır [11]- [19]. Yapay zeka temelli teknolojiler, tarım alanlarında yabancı ot tespiti, sınıflandırılması ve kontrolü gibi önemli mücadele alanlarında inovatif ve akıllı çözümler sunabilir.…”

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Yabancı Otlarla Mücadelede Yapay Zekanın Rolü

2023

Proceeding Book of 2nd International Conference on Recent Academic Studies ICRAS 2023

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Chess playing robotic arm

Cited by 5 publications

References 3 publications

Performance Evaluation of Jaccard-Dice Coefficient on Building Segmentation from High Resolution Satellite Images

Performance Evaluation of Jaccard-Dice Coefficient on Building Segmentation from High Resolution Satellite Images

Pattern recognition based movement control and gripping forces control system on arm robot model using LabVIEW

Yabancı Otlarla Mücadelede Yapay Zekanın Rolü

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